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Dr. Qing Tian is seeking applicants for PhD positions in Computer Vision, Deep Learning, and AI (starting Fall 2024, Spring/Fall 2025).

Qing Tian is currently an Assistant Professor of Computer Science at the University of Alabama at Birmingham, AL, USA. He obtained his Ph.D. from the Department of Electrical and Computer Engineering at McGill University, QC, Canada, under the co-supervision of James Clark and Tal Arbel. His primary research interests lie in Computer Vision and Machine Learning, particularly neural network compression; autonomous driving perception; neural architecture search; adversarial AI.

Prior to joining UAB, he used to work at Bowling Green State University (as an assistant professor), Amazon Visual Search (as an applied scientist intern) and Nakisa Inc (as a software developer).

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Research Interests

• Deep Neural Network Compression (e.g., pruning and knowledge distillation)
• Fast and Robust Visual Detection in Self-driving Vehicles
• Explainable AI (XAI)

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Research projects

• Grant No.: NSF 2153404, Investigators: Qing Tian, 2022 - 2024, National Science Foundation, CRII:RI: Deep neural network pruning for fast and reliable visual detection in self-driving vehicles, Role: PI, $149,343.00, Awarded Level: Federal

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Publications

Journals

• Tian, Q., Arbel, T., & Clark, J. J. (2023). Grow-Push-Prune: aligning deep discriminants for effective structural network compression. Computer Vision and Image Understanding (CVIU), vol. 231, p. 103682, June 2023, DOI: 10.1016/j.cviu.2023.103682.
• Lan, Q., & Tian, Q. (2023). Instance, Scale, and Teacher Adaptive Knowledge Distillation for Visual Detection in Autonomous Driving. IEEE Transactions on Intelligent Vehicles, vol. 8, no. 3, pp. 2358-2370, March 2023, DOI: 10.1109/TIV.2022.3217261.
• Islam, M. K., Simic Milas, A., Abeysinghe, T., & Tian, Q. (2023). Integrating UAV and WorldView-3 Imagery for Mapping Wetland Plants in the Old Woman Creek Estuary, USA. Remote Sensing, vol. 15, no. 4, p. 1090, Feb. 2023, DOI: 10.3390/rs15041090.
• Tian, Q., Chanda, S., Kumar KC, A., & Gray, D. (2023). Improving apparel detection with category grouping and multi-grained branches. Multimedia Tools and Applications, vol. 82, no. 5, pp. 7383-7400, Feb. 2023, DOI: 10.1007/s11042-022-13424-8.
• Tian, Q., Arbel, T., & Clark, J. J. (2021). Task dependent deep LDA pruning of neural networks. Computer Vision and Image Understanding (CVIU), vol. 203, p. 103154, Feb. 2021, DOI: 10.1016/j.cviu.2020.103154.
• Tian, Q., Arbel, T., & Clark, J. J. (2018). Structured deep Fisher pruning for efficient facial trait classification. Image and Vision Computing (IVC), vol. 77, pp. 45-59, Sept. 2018, DOI:10.1016/j.imavis.2018.06.008.

Conferences

• Nadella, S., Barua, P., Hagler, J. C., Lamb, D. J., & Tian, Q. (2024). Enhancing Accuracy and Robustness of Steering Angle Prediction with Attention Mechanism. 35th IEEE Intelligent Vehicles Symposium (IV), accepted.
• Lan, Q., & Tian, Q. (2024). Gradient-Guided Knowledge Distillation for Object Detectors. In Proceedings of the 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 424-433).
• Zhu, L., Lan, Q., Velasquez, A., Song, H., Kamal, A., Tian, Q., and & Niu, S. (2023). SKGHOI: Spatial-Semantic Knowledge Graph for Human-Object Interaction Detection. Machine Learning on Graphs Workshop held in conjunction with the 23rd IEEE International Conference on Data Mining (ICDM) (pp. 1186-1193).
• Choi, J., & Tian, Q. (2023). Visual-Saliency-Guided Channel Pruning for Deep Visual Detectors in Autonomous Driving. In Proceedings of the 34th IEEE Intelligent Vehicles Symposium (IV) (pp. 1-6).
• Choi, J., & Tian, Q. (2022). Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarios. In Proceedings of the 33rd IEEE Intelligent Vehicles Symposium (IV) (pp. 1011-1017).
• Lan, Q., & Tian, Q. (2022). Adaptive Instance Distillation for Object Detection in Autonomous Driving. In Proceedings of the 26th International Conference on Pattern Recognition (ICPR) (pp. 4559-4565).
• Moradi, S., Lee, J. K., & Tian, Q. (2021). Exploration of U-Net in Automated Solar Coronal Loop Segmentation. In Proceedings of the 29th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (pp. 227-236).
• Tian, Q., Arbel, T., & Clark, J. J. (2017). Deep lda-pruned nets for efficient facial gender classification. In Proceedings of the IEEE Computer Society Workshop on Biometrics held in conjunction with IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 10-19).
• Tian, Q., Arbel, T., & Clark, J. J. (2016). Shannon information based adaptive sampling for action recognition. In Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) (pp. 967-972).
• Tian, Q., & Clark, J. J. (2013). Real-time specularity detection using unnormalized wiener entropy. In Proceedings of the 2013 International Conference on Computer and Robot Vision (CRV) (pp. 356-363).

(Click here for my Google Scholar Profile, which includes my arxiv papers)

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Teaching

• Machine Learning, Unsupervised Feature Learning, Artificial Intelligence Methods
• Data Science Programming, Python for Computational and Data Science
• Other courses: Algorithms and Data Structures, Programming Fundamentals, Software Engineering, Pro. & Soc. Issues in Computing